{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'BUFSIZE',\n",
       " 'CLIP',\n",
       " 'DataSource',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'WRAP',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_builtins',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_typing',\n",
       " '_using_numpy2_behavior',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'expm1x',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000004.SZ   20241225  14.63  14.63  14.63  14.63      16.25   -1.62   \n",
      "1     000004.SZ   20241224  16.25  16.58  16.25  16.25      18.05   -1.80   \n",
      "2     000004.SZ   20241223  18.11  19.41  18.05  18.05      20.06   -2.01   \n",
      "3     000004.SZ   20241220  19.62  20.59  19.28  20.06      20.63   -0.57   \n",
      "4     000004.SZ   20241219  18.75  20.63  18.74  20.63      18.75    1.88   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3384  000004.SZ   20100224   9.10   9.45   9.02   9.40       9.10    0.30   \n",
      "3385  000004.SZ   20100223   9.10   9.22   8.88   9.10       9.22   -0.12   \n",
      "3386  000004.SZ   20100222   9.09   9.30   8.82   9.22       9.17    0.05   \n",
      "3387  000004.SZ   20100212   9.10   9.34   9.03   9.17       9.50   -0.33   \n",
      "3388  000004.SZ   20100211   9.50   9.94   9.50   9.50      10.00   -0.50   \n",
      "\n",
      "      pct_chg        vol       amount  \n",
      "0     -9.9692   35453.00   51867.7390  \n",
      "1     -9.9723   63387.00  103125.2060  \n",
      "2    -10.0199  252984.24  466472.4850  \n",
      "3     -2.7630  319186.05  638201.8500  \n",
      "4     10.0267  366653.18  751937.6560  \n",
      "...       ...        ...          ...  \n",
      "3384   3.3000   13111.33   12122.3881  \n",
      "3385  -1.3000   17904.55   16210.6835  \n",
      "3386   0.5500   24813.10   22488.1075  \n",
      "3387  -3.4700   32126.76   29186.3041  \n",
      "3388  -5.0000   37140.43   35341.7808  \n",
      "\n",
      "[3389 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('3facfac1b107b1791fcdfcec99648d838f0c464a74e3b2544c7efd1f')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000004.sz\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000004.SZ   20241225  14.63  14.63  14.63  14.63      16.25   -1.62   \n",
      "1     000004.SZ   20241224  16.25  16.58  16.25  16.25      18.05   -1.80   \n",
      "2     000004.SZ   20241223  18.11  19.41  18.05  18.05      20.06   -2.01   \n",
      "3     000004.SZ   20241220  19.62  20.59  19.28  20.06      20.63   -0.57   \n",
      "4     000004.SZ   20241219  18.75  20.63  18.74  20.63      18.75    1.88   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3384  000004.SZ   20100224   9.10   9.45   9.02   9.40       9.10    0.30   \n",
      "3385  000004.SZ   20100223   9.10   9.22   8.88   9.10       9.22   -0.12   \n",
      "3386  000004.SZ   20100222   9.09   9.30   8.82   9.22       9.17    0.05   \n",
      "3387  000004.SZ   20100212   9.10   9.34   9.03   9.17       9.50   -0.33   \n",
      "3388  000004.SZ   20100211   9.50   9.94   9.50   9.50      10.00   -0.50   \n",
      "\n",
      "      pct_chg        vol       amount  \n",
      "0     -9.9692   35453.00   51867.7390  \n",
      "1     -9.9723   63387.00  103125.2060  \n",
      "2    -10.0199  252984.24  466472.4850  \n",
      "3     -2.7630  319186.05  638201.8500  \n",
      "4     10.0267  366653.18  751937.6560  \n",
      "...       ...        ...          ...  \n",
      "3384   3.3000   13111.33   12122.3881  \n",
      "3385  -1.3000   17904.55   16210.6835  \n",
      "3386   0.5500   24813.10   22488.1075  \n",
      "3387  -3.4700   32126.76   29186.3041  \n",
      "3388  -5.0000   37140.43   35341.7808  \n",
      "\n",
      "[3389 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock601939.csv\")\n",
    "stockname='国农科技'\n",
    "stockcode='000004'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3379</th>\n",
       "      <th>3380</th>\n",
       "      <th>3381</th>\n",
       "      <th>3382</th>\n",
       "      <th>3383</th>\n",
       "      <th>3384</th>\n",
       "      <th>3385</th>\n",
       "      <th>3386</th>\n",
       "      <th>3387</th>\n",
       "      <th>3388</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100303</td>\n",
       "      <td>20100302</td>\n",
       "      <td>20100301</td>\n",
       "      <td>20100226</td>\n",
       "      <td>20100225</td>\n",
       "      <td>20100224</td>\n",
       "      <td>20100223</td>\n",
       "      <td>20100222</td>\n",
       "      <td>20100212</td>\n",
       "      <td>20100211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>14.63</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.11</td>\n",
       "      <td>19.62</td>\n",
       "      <td>18.75</td>\n",
       "      <td>18.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.88</td>\n",
       "      <td>20.03</td>\n",
       "      <td>18.35</td>\n",
       "      <td>...</td>\n",
       "      <td>11.38</td>\n",
       "      <td>10.95</td>\n",
       "      <td>10.62</td>\n",
       "      <td>9.96</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>14.63</td>\n",
       "      <td>16.58</td>\n",
       "      <td>19.41</td>\n",
       "      <td>20.59</td>\n",
       "      <td>20.63</td>\n",
       "      <td>19.12</td>\n",
       "      <td>20.19</td>\n",
       "      <td>20.7</td>\n",
       "      <td>20.84</td>\n",
       "      <td>20.03</td>\n",
       "      <td>...</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.42</td>\n",
       "      <td>10.88</td>\n",
       "      <td>10.36</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>14.63</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.05</td>\n",
       "      <td>19.28</td>\n",
       "      <td>18.74</td>\n",
       "      <td>17.18</td>\n",
       "      <td>18.5</td>\n",
       "      <td>19.4</td>\n",
       "      <td>19.65</td>\n",
       "      <td>18.3</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.95</td>\n",
       "      <td>10.6</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>14.63</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.05</td>\n",
       "      <td>20.06</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.75</td>\n",
       "      <td>18.55</td>\n",
       "      <td>20.56</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.03</td>\n",
       "      <td>...</td>\n",
       "      <td>11.28</td>\n",
       "      <td>11.42</td>\n",
       "      <td>10.88</td>\n",
       "      <td>10.36</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>16.25</td>\n",
       "      <td>18.05</td>\n",
       "      <td>20.06</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.75</td>\n",
       "      <td>18.55</td>\n",
       "      <td>20.56</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.03</td>\n",
       "      <td>18.21</td>\n",
       "      <td>...</td>\n",
       "      <td>11.42</td>\n",
       "      <td>10.88</td>\n",
       "      <td>10.36</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.82</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-9.9692</td>\n",
       "      <td>-9.9723</td>\n",
       "      <td>-10.0199</td>\n",
       "      <td>-2.763</td>\n",
       "      <td>10.0267</td>\n",
       "      <td>1.0782</td>\n",
       "      <td>-9.7763</td>\n",
       "      <td>2.5436</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>9.9945</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5.02</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>35453.0</td>\n",
       "      <td>63387.0</td>\n",
       "      <td>252984.24</td>\n",
       "      <td>319186.05</td>\n",
       "      <td>366653.18</td>\n",
       "      <td>143819.77</td>\n",
       "      <td>166664.47</td>\n",
       "      <td>237036.21</td>\n",
       "      <td>351013.04</td>\n",
       "      <td>143054.98</td>\n",
       "      <td>...</td>\n",
       "      <td>31608.86</td>\n",
       "      <td>51316.44</td>\n",
       "      <td>16789.06</td>\n",
       "      <td>21279.37</td>\n",
       "      <td>28143.77</td>\n",
       "      <td>13111.33</td>\n",
       "      <td>17904.55</td>\n",
       "      <td>24813.1</td>\n",
       "      <td>32126.76</td>\n",
       "      <td>37140.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>51867.739</td>\n",
       "      <td>103125.206</td>\n",
       "      <td>466472.485</td>\n",
       "      <td>638201.85</td>\n",
       "      <td>751937.656</td>\n",
       "      <td>258315.553</td>\n",
       "      <td>322013.478</td>\n",
       "      <td>480549.269</td>\n",
       "      <td>710849.839</td>\n",
       "      <td>283291.718</td>\n",
       "      <td>...</td>\n",
       "      <td>35474.6542</td>\n",
       "      <td>58441.3395</td>\n",
       "      <td>18214.4604</td>\n",
       "      <td>21673.0221</td>\n",
       "      <td>27243.276</td>\n",
       "      <td>12122.3881</td>\n",
       "      <td>16210.6835</td>\n",
       "      <td>22488.1075</td>\n",
       "      <td>29186.3041</td>\n",
       "      <td>35341.7808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3389 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0           1           2          3           4     \\\n",
       "ts_code     000004.SZ   000004.SZ   000004.SZ  000004.SZ   000004.SZ   \n",
       "trade_date   20241225    20241224    20241223   20241220    20241219   \n",
       "open            14.63       16.25       18.11      19.62       18.75   \n",
       "high            14.63       16.58       19.41      20.59       20.63   \n",
       "low             14.63       16.25       18.05      19.28       18.74   \n",
       "close           14.63       16.25       18.05      20.06       20.63   \n",
       "pre_close       16.25       18.05       20.06      20.63       18.75   \n",
       "change          -1.62        -1.8       -2.01      -0.57        1.88   \n",
       "pct_chg       -9.9692     -9.9723    -10.0199     -2.763     10.0267   \n",
       "vol           35453.0     63387.0   252984.24  319186.05   366653.18   \n",
       "amount      51867.739  103125.206  466472.485  638201.85  751937.656   \n",
       "\n",
       "                  5           6           7           8           9     ...  \\\n",
       "ts_code      000004.SZ   000004.SZ   000004.SZ   000004.SZ   000004.SZ  ...   \n",
       "trade_date    20241218    20241217    20241216    20241213    20241212  ...   \n",
       "open             18.37        20.0       19.88       20.03       18.35  ...   \n",
       "high             19.12       20.19        20.7       20.84       20.03  ...   \n",
       "low              17.18        18.5        19.4       19.65        18.3  ...   \n",
       "close            18.75       18.55       20.56       20.05       20.03  ...   \n",
       "pre_close        18.55       20.56       20.05       20.03       18.21  ...   \n",
       "change             0.2       -2.01        0.51        0.02        1.82  ...   \n",
       "pct_chg         1.0782     -9.7763      2.5436      0.0999      9.9945  ...   \n",
       "vol          143819.77   166664.47   237036.21   351013.04   143054.98  ...   \n",
       "amount      258315.553  322013.478  480549.269  710849.839  283291.718  ...   \n",
       "\n",
       "                  3379        3380        3381        3382       3383  \\\n",
       "ts_code      000004.SZ   000004.SZ   000004.SZ   000004.SZ  000004.SZ   \n",
       "trade_date    20100303    20100302    20100301    20100226   20100225   \n",
       "open             11.38       10.95       10.62        9.96       9.44   \n",
       "high             11.41       11.42       10.88       10.36       9.87   \n",
       "low               11.0       10.95        10.6        9.87       9.35   \n",
       "close            11.28       11.42       10.88       10.36       9.87   \n",
       "pre_close        11.42       10.88       10.36        9.87        9.4   \n",
       "change           -0.14        0.54        0.52        0.49       0.47   \n",
       "pct_chg          -1.23        4.96        5.02        4.96        5.0   \n",
       "vol           31608.86    51316.44    16789.06    21279.37   28143.77   \n",
       "amount      35474.6542  58441.3395  18214.4604  21673.0221  27243.276   \n",
       "\n",
       "                  3384        3385        3386        3387        3388  \n",
       "ts_code      000004.SZ   000004.SZ   000004.SZ   000004.SZ   000004.SZ  \n",
       "trade_date    20100224    20100223    20100222    20100212    20100211  \n",
       "open               9.1         9.1        9.09         9.1         9.5  \n",
       "high              9.45        9.22         9.3        9.34        9.94  \n",
       "low               9.02        8.88        8.82        9.03         9.5  \n",
       "close              9.4         9.1        9.22        9.17         9.5  \n",
       "pre_close          9.1        9.22        9.17         9.5        10.0  \n",
       "change             0.3       -0.12        0.05       -0.33        -0.5  \n",
       "pct_chg            3.3        -1.3        0.55       -3.47        -5.0  \n",
       "vol           13111.33    17904.55     24813.1    32126.76    37140.43  \n",
       "amount      12122.3881  16210.6835  22488.1075  29186.3041  35341.7808  \n",
       "\n",
       "[11 rows x 3389 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dc40834530>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601939.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3359 entries, 0 to 3358\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3359 non-null   datetime64[ns]\n",
      " 1   open        3359 non-null   float64       \n",
      " 2   high        3359 non-null   float64       \n",
      " 3   low         3359 non-null   float64       \n",
      " 4   close       3359 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 131.3 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3354</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>18.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.74</td>\n",
       "      <td>20.63</td>\n",
       "      <td>19.708</td>\n",
       "      <td>19.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>19.62</td>\n",
       "      <td>20.59</td>\n",
       "      <td>19.28</td>\n",
       "      <td>20.06</td>\n",
       "      <td>19.710</td>\n",
       "      <td>19.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>18.11</td>\n",
       "      <td>19.41</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.05</td>\n",
       "      <td>19.208</td>\n",
       "      <td>19.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.748</td>\n",
       "      <td>19.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>17.924</td>\n",
       "      <td>18.756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10\n",
       "3354 2024-12-19  18.75  20.63  18.74  20.63  19.708  19.168\n",
       "3355 2024-12-20  19.62  20.59  19.28  20.06  19.710  19.325\n",
       "3356 2024-12-23  18.11  19.41  18.05  18.05  19.208  19.307\n",
       "3357 2024-12-24  16.25  16.58  16.25  16.25  18.748  19.114\n",
       "3358 2024-12-25  14.63  14.63  14.63  14.63  17.924  18.756"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-02-11   9.50   9.94   9.50   9.50\n",
      "1    2010-02-12   9.10   9.34   9.03   9.17\n",
      "2    2010-02-22   9.09   9.30   8.82   9.22\n",
      "3    2010-02-23   9.10   9.22   8.88   9.10\n",
      "4    2010-02-24   9.10   9.45   9.02   9.40\n",
      "...         ...    ...    ...    ...    ...\n",
      "3384 2024-12-19  18.75  20.63  18.74  20.63\n",
      "3385 2024-12-20  19.62  20.59  19.28  20.06\n",
      "3386 2024-12-23  18.11  19.41  18.05  18.05\n",
      "3387 2024-12-24  16.25  16.58  16.25  16.25\n",
      "3388 2024-12-25  14.63  14.63  14.63  14.63\n",
      "\n",
      "[3389 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3389 entries, 0 to 3388\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3389 non-null   datetime64[ns]\n",
      " 1   open        3389 non-null   float64       \n",
      " 2   high        3389 non-null   float64       \n",
      " 3   low         3389 non-null   float64       \n",
      " 4   close       3389 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 132.5 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-02-22</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.30</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-02-23</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.22</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-02-24</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.40</td>\n",
       "      <td>9.278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>18.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.74</td>\n",
       "      <td>20.63</td>\n",
       "      <td>19.708</td>\n",
       "      <td>19.168</td>\n",
       "      <td>18.606000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>19.62</td>\n",
       "      <td>20.59</td>\n",
       "      <td>19.28</td>\n",
       "      <td>20.06</td>\n",
       "      <td>19.710</td>\n",
       "      <td>19.325</td>\n",
       "      <td>18.630667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>18.11</td>\n",
       "      <td>19.41</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.05</td>\n",
       "      <td>19.208</td>\n",
       "      <td>19.307</td>\n",
       "      <td>18.552333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3387</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.748</td>\n",
       "      <td>19.114</td>\n",
       "      <td>18.438000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>17.924</td>\n",
       "      <td>18.756</td>\n",
       "      <td>18.273333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3389 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "0    2010-02-11   9.50   9.94   9.50   9.50     NaN     NaN        NaN\n",
       "1    2010-02-12   9.10   9.34   9.03   9.17     NaN     NaN        NaN\n",
       "2    2010-02-22   9.09   9.30   8.82   9.22     NaN     NaN        NaN\n",
       "3    2010-02-23   9.10   9.22   8.88   9.10     NaN     NaN        NaN\n",
       "4    2010-02-24   9.10   9.45   9.02   9.40   9.278     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "3384 2024-12-19  18.75  20.63  18.74  20.63  19.708  19.168  18.606000\n",
       "3385 2024-12-20  19.62  20.59  19.28  20.06  19.710  19.325  18.630667\n",
       "3386 2024-12-23  18.11  19.41  18.05  18.05  19.208  19.307  18.552333\n",
       "3387 2024-12-24  16.25  16.58  16.25  16.25  18.748  19.114  18.438000\n",
       "3388 2024-12-25  14.63  14.63  14.63  14.63  17.924  18.756  18.273333\n",
       "\n",
       "[3389 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          14.63  14.63\n",
       "1          16.25  16.58\n",
       "2          18.11  19.41\n",
       "3          19.62  20.59\n",
       "4          18.75  20.63\n",
       "...          ...    ...\n",
       "3384        9.10   9.45\n",
       "3385        9.10   9.22\n",
       "3386        9.09   9.30\n",
       "3387        9.10   9.34\n",
       "3388        9.50   9.94\n",
       "\n",
       "[3389 rows x 2 columns]>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.5 ],\n",
       "       [ 9.17],\n",
       "       [ 9.22],\n",
       "       ...,\n",
       "       [18.05],\n",
       "       [16.25],\n",
       "       [14.63]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14651.0</th>\n",
       "      <td>9.50</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14652.0</th>\n",
       "      <td>9.10</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14662.0</th>\n",
       "      <td>9.09</td>\n",
       "      <td>9.30</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14663.0</th>\n",
       "      <td>9.10</td>\n",
       "      <td>9.22</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14664.0</th>\n",
       "      <td>9.10</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>18.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.74</td>\n",
       "      <td>20.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>19.62</td>\n",
       "      <td>20.59</td>\n",
       "      <td>19.28</td>\n",
       "      <td>20.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>18.11</td>\n",
       "      <td>19.41</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>16.25</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3389 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14651.0   9.50   9.94   9.50   9.50\n",
       "14652.0   9.10   9.34   9.03   9.17\n",
       "14662.0   9.09   9.30   8.82   9.22\n",
       "14663.0   9.10   9.22   8.88   9.10\n",
       "14664.0   9.10   9.45   9.02   9.40\n",
       "...        ...    ...    ...    ...\n",
       "20076.0  18.75  20.63  18.74  20.63\n",
       "20077.0  19.62  20.59  19.28  20.06\n",
       "20080.0  18.11  19.41  18.05  18.05\n",
       "20081.0  16.25  16.58  16.25  16.25\n",
       "20082.0  14.63  14.63  14.63  14.63\n",
       "\n",
       "[3389 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14651.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14652.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14662.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.30</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14663.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.22</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14664.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>18.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.74</td>\n",
       "      <td>20.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>19.62</td>\n",
       "      <td>20.59</td>\n",
       "      <td>19.28</td>\n",
       "      <td>20.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>18.11</td>\n",
       "      <td>19.41</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3387</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3389 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14651.0   9.50   9.94   9.50   9.50\n",
       "1     14652.0   9.10   9.34   9.03   9.17\n",
       "2     14662.0   9.09   9.30   8.82   9.22\n",
       "3     14663.0   9.10   9.22   8.88   9.10\n",
       "4     14664.0   9.10   9.45   9.02   9.40\n",
       "...       ...    ...    ...    ...    ...\n",
       "3384  20076.0  18.75  20.63  18.74  20.63\n",
       "3385  20077.0  19.62  20.59  19.28  20.06\n",
       "3386  20080.0  18.11  19.41  18.05  18.05\n",
       "3387  20081.0  16.25  16.58  16.25  16.25\n",
       "3388  20082.0  14.63  14.63  14.63  14.63\n",
       "\n",
       "[3389 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dc45f08920>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ana\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.0991 - val_loss: 0.0017\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0179 - val_loss: 0.0019\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0022 - val_loss: 0.0014\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0011 - val_loss: 0.0015\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 0.0014\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.8690e-04 - val_loss: 0.0014\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.6072e-04 - val_loss: 0.0014\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.3883e-04 - val_loss: 0.0014\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.2154e-04 - val_loss: 0.0013\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.0351e-04 - val_loss: 0.0013\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.8524e-04 - val_loss: 0.0013\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.6747e-04 - val_loss: 0.0013\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.4984e-04 - val_loss: 0.0013\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.2962e-04 - val_loss: 0.0013\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.0694e-04 - val_loss: 0.0012\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.8333e-04 - val_loss: 0.0012\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.6036e-04 - val_loss: 0.0012\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3795e-04 - val_loss: 0.0012\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.1874e-04 - val_loss: 0.0012\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.0000e-04 - val_loss: 0.0012\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.8513e-04 - val_loss: 0.0011\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.7286e-04 - val_loss: 0.0011\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.6115e-04 - val_loss: 0.0011\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.5191e-04 - val_loss: 0.0011\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.4198e-04 - val_loss: 0.0011\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.2855e-04 - val_loss: 0.0011\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.1747e-04 - val_loss: 0.0011\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.1100e-04 - val_loss: 0.0011\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.0273e-04 - val_loss: 0.0011\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.9424e-04 - val_loss: 0.0011\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e6Q0KXzjT3t6uUaNG6cknn9T111+fdDussNyT4nQ6tWTJEtXW1mrdunUKhUJatmyZJGnp0qWqrq7u0yNSUlKixYsXa+XKlSosLFRBQYFuueWWyPuNjY06evSoKioq7Pk0NnIUdvcQca8UAKe4fKehJz4zISv7terSSy/VPffco5dfflkjR47UmDFjJHX1svz4xz/Wr371K40dO1ajR49WZ2dnSu1qamrSyJEjI9Njx46NvE7H/hoaGqL2kZ+frxEjRqihoUHDhg3TkCFDIn/Mh3tmEjllMtD+vF6v8vPz1dDQoAsuuEAPPfSQ/v3f/13f+MY3NH36dK1cuVLl5eUaM2aM7r//ft17771666239IlPfEL33nuvzjorfeM2E7rjbFVVlWpra7V3715NnDgx8qjvBx98sN91Zs+ercrKStXX12vy5MlR90Hxer16/PHHk2x6ehmRMSl+Dc6L9gDAGsMwLJ12yaYxY8aorKxMa9eujepFeeGFFyKnQYYPH65nnnlGf/nLX1La11lnnaXGxsbIdHh8iNX9JXqp96hRo3Tw4MHIdFtbm95++22NGjVKbW1tcYdVJGPUqFFRQzGOHDmiEydOaNSoUWppadGZZ56p//iP/5Df79cdd9yhZcuW6dFHH1VDQ4MmT56sp556Su+9956+8IUvaPXq1VqxYoWt7esp4Wf3lJaWavr06ZGAYoXX69W0adNy40ZtFp083UNPCgDkgksuuUTPPfecLrnkksi848ePS+q6LPnVV1/V8uXLU+plkLp6bd566y1t2LBB+/fv1/e+972E9ldeXq4dO3bo7bff1h//+McBe1r+4R/+QcePH9cDDzyg+vp63X333SovL9cFF1yQ0ufo6OhQQ0ND1Fd7e7uuvfZa/elPf9Ivf/lLHTx4UHfddZeuuOIKDR8+XC0tLbruuuv07LPP6tixY3I4HAqFQpKkPXv2aNGiRXr11Vfl9/tlGEbkvXThAYP9iNzQjbvOAkBOuPTSS+VyuTRjxozIvMsuu0yzZs3SFVdcoTvvvFMLFy7U22+/rebm5qT3U1ZWpjVr1uj73/++5s6dq+nTpye0v9tuu0319fX66Ec/qrvuumvAX+RFRUX65S9/qe3bt+vyyy9XQ0ODHnnkETkcqf2K/r//+z9Nnz496uuvf/2rRo4cqccee0w///nPNXv2bBUWFuqBBx6Q1HWRzLe+9S3ddddduvjii7V3715985vflNRV/0WLFukLX/iCPv7xj8s0TX3lK19JqY0DMcxUI2cOaG5utvVacMMw5PnDr3XsN4/JmHWNHP/fTbZt+1RmGIbKysp05MiRlP+SOZ1Qt8RRs+SE67Z79+6EesPRdQuMXLjnyGDT1tYmj8fT59+p2+229z4pp5vI6R4uQQYAICsIKf0wuAQZAICsIqT0w+E5eXUPAADIPEJKPwyu7gEAIKsIKf1gTAoAAKlJ9J4xvRFS+sGYFACnsnTf3wIIhUKElHSJ3BafnhQAp5jCwkIdO3aMoIK0CYVCOnbsmEaMGJHSdhK6Lf7ppOcdZ03TTDkNAkCucLvdKioqUmtra7abMmjk5eWpvb09280YVIqKiuTxePTee+8lvQ1CSj8ip3tCIam9Xep++iMAnApcLhc3dLOImwcmx44/7jnd0w+jwCOFC8wVPgAAZBwhpR+GYUgF3Q9EZFwKAAAZR0iJx+Pp+k5PCgAAGUdIiYeeFAAAsoaQEo+nO6RwrxQAADKOkBKH0d2TwvN7AADIPEJKPIxJAQAgawgp8TAmBQCArCGkxBMek3KCMSkAAGQaISWeSE8KIQUAgEwjpMRhRK7u4XQPAACZRkiJp6Br4CxX9wAAkHmElHi4TwoAAFlDSInHw9U9AABkCyEljvDN3BiTAgBA5hFS4qEnBQCArCGkxFMQvuMsY1IAAMg0Qko84Z6UYLvMjmB22wIAwGmGkBJPvufka3pTAADIKEJKHIbTKeXld00wLgUAgIwipAyEe6UAAJAVhJSB8CRkAACygpAykMgVPoQUAAAyyZXIwgcPHtTatWvV2NiomTNnatGiRTIMI+46u3bt0sMPPyyfz6e5c+dqzpw5fZZZvXq1SkpKdNNNNyXW+kzoPt1jBvyK/0kBAICdLPekBINB1dTUqLy8XKtWrVJ9fb22bdsWdx2fz6eamhrNmDFDK1as0I4dO1RXVxe1zOuvv666ujp95jOfSeoDpF0BY1IAAMgGyyFl586d8vv9qq6ultfr1YIFC7R169a46+zYsUNDhw7VvHnzVFZWpvnz50et097erp/+9KdauHChioqKkv8UaWR4ON0DAEA2WD7dc+DAAU2YMEH5+V2X5I4bN0719fUDrjNlypTIKaGKigqtX78+8v7GjRvV3t4up9Opuro6nX/++XFPHwWDQQWDJ2+qZhiGPN0hYqDTTokIb8swDMnTHZ7aArbu41QUVTdYRt0SR82SQ92SQ92SY0fdLIeUQCCg4cOHR+3c4XCotbVVxcXFMdfx+/0aPXp0ZNrj8ailpUWS9M477+g///M/VVFRoXfeeUe///3vddZZZ+lf/uVf+v1AmzZt0saNGyPT5eXlqqmpiWqXnbxer947a7iOSSpyGBpaVpaW/ZxqvF5vtpswKFG3xFGz5FC35FC35KRSN8shxeFwyO12R83Ly8tTe3t7v+s4nU65XK6Yy2/btk1nnnmmvvnNb8rtduvKK6/Ul770Jf3lL39RZWVlzO31HngbDjPNzc3q6Oiw+lEGZBiGvF6vGhsb1dkZkiQdf6dZbUeO2LaPU1HPupmmme3mDBrULXHULDnULTnULTnx6uZyuSx1MFgOKcXFxTp06FDUvEAgEBVCYq3j8/liLv/uu+9qypQpkeDj8XhUVlampqamfrfndrv7BKWwdBw4pmnK7L4E2Wzzc3BaZJomtUoCdUscNUsOdUsOdUtOKnWzPHC2oqJCe/bsiUw3NTUpGAz2e6pHksaPHx+1zv79+1VaWipJGjZsWFQvTCgU0rvvvpu2UzdJ42ZuAABkheWQMmnSJPn9fm3fvl2StHnzZk2dOlUOh0N+vz/m6ZZp06Zp9+7dqqurU2dnp7Zs2RI5lXPxxRfrz3/+s1566SW9++67Wr9+vTo6OjRx4kSbPpo9DG6LDwBAVlg+3eN0OrVkyRLV1tZq3bp1CoVCWrZsmSRp6dKlqq6uVlVVVdQ6JSUlWrx4sVauXKnCwkIVFBTolltukSSNHDlSt99+ux5//HEdPnxYXq9XS5cujVytkzPoSQEAICsSuuNsVVWVamtrtXfvXk2cOFElJSWSpAcffLDfdWbPnq3KykrV19dr8uTJKiwsjLx34YUX6sILL0yy6RnCfVIAAMiKhEKKJJWWlkbGlVjl9XoH76Vb3HEWAICs4AGDA+kxJsUMhbLbFgAATiOElIEUnDw9pRNt2WsHAACnGULKAAy3WwrfC4bBswAAZAwhxYrIuBRCCgAAmUJIscLDZcgAAGQaIcWKgvBlyFzhAwBAphBSrPBwugcAgEwjpFjRPSbF5HQPAAAZQ0ixwGDgLAAAGUdIsSI8JiXAmBQAADKFkGIFz+8BACDjCClW8CRkAAAyjpBihYeHDAIAkGmEFCu4ugcAgIwjpFhgMCYFAICMI6RYwZgUAAAyjpBiBWNSAADIOEKKFfSkAACQcYQUK3qMSTFNM7ttAQDgNEFIsSLckxIKSe3t2W0LAACnCUKKFfkFkmF0veYKHwAAMoKQYoFhGIxLAQAgwwgpVnGvFAAAMoqQYhU9KQAAZBQhxSrulQIAQEYRUqwq6DrdYxJSAADICEKKRUb4dA9jUgAAyAhCilUexqQAAJBJhBSr6EkBACCjCClWhS9BDjAmBQCATCCkWEVPCgAAGUVIsap7TIrJmBQAADKCkGJVAfdJAQAgkwgpFhmRMSn0pAAAkAmuRBY+ePCg1q5dq8bGRs2cOVOLFi3qevheHLt27dLDDz8sn8+nuXPnas6cOZH37rvvPr322muR6alTp+pb3/pWgh8hQxiTAgBARlkOKcFgUDU1NaqsrNRtt92mn/3sZ9q2bZsuu+yyftfx+XyqqanR1VdfrRkzZmj16tU655xzNGXKFEnSvn379N3vflfDhg2TJDmdzhQ/ThoV0JMCAEAmWT7ds3PnTvn9flVXV8vr9WrBggXaunVr3HV27NihoUOHat68eSorK9P8+fMj67z77rsyTVNjx45VUVGRioqKVFBQkNqnSSee3QMAQEZZDikHDhzQhAkTlJ+fL0kaN26c6uvrB1xnypQpkVNCFRUV2rdvnyTpzTffVCgU0i233KIbbrhBq1evVmtra7KfI/3Cp3uC7TI7gtltCwAApwHLp3sCgYCGDx8emTYMQw6HQ62trSouLo65jt/v1+jRoyPTHo9HLS0tkqQjR47o3HPP1Q033CDDMLR27Vr96le/0uc///l+2xAMBhUMngwIhmHI0z2gdaCxMYkIbytqm+GeFEnGiTYZ7jzb9neqiFk3DIi6JY6aJYe6JYe6JceOulkOKQ6HQ263O2peXl6e2tvb+13H6XTK5XLFXP6aa67RNddcE3nv+uuv1/e+9724IWXTpk3auHFjZLq8vFw1NTVR4clOXq83aro+v0DmiTadfUaxXN6ytOzzVNC7brCGuiWOmiWHuiWHuiUnlbpZDinFxcU6dOhQ1LxAIBAVQmKt4/P5LC1fWFioY8eOKRgM9glDYb2vDgqns+bmZnV0dFj9KAMyDENer1eNjY0yTTMy3yzwSCfa1HTwgAyTq7d7669uiI+6JY6aJYe6JYe6JSde3Vwul6UOBsshpaKiImqgbFNTk4LBYL+neiRp/Pjxev755yPT+/fvV2lpqSTpe9/7nq6++mpNmDBBkrR3714NGTKk34AiSW63u9/303HgmKYZvd2CQun9ozL9xyUO1H71qRssoW6Jo2bJoW7JoW7JSaVulrsDJk2aJL/fr+3bt0uSNm/erKlTp8rhcMjv98fsyZg2bZp2796turo6dXZ2asuWLaqsrJTUNfD20Ucf1Z49e/TnP/9ZTzzxhGbNmpXUh8iY8GXI3CsFAIC0s9yT4nQ6tWTJEtXW1mrdunUKhUJatmyZJGnp0qWqrq5WVVVV1DolJSVavHixVq5cqcLCQhUUFOiWW26R1DUmpbm5WStWrFBJSYlmzZqluXPn2vfJ0qHH83sYPgUAQHoldMfZqqoq1dbWau/evZo4caJKSkokSQ8++GC/68yePVuVlZWqr6/X5MmTVVjY9Yve5XLpi1/8or74xS+m0PwM4/k9AABkTEIhRZJKS0sj40qs8nq9p8SoaMPjkSlxugcAgAzgEpVEhHtSuDU+AABpR0hJBLfGBwAgYwgpiaAnBQCAjCGkJKL7FvwmY1IAAEg7Qkoi6EkBACBjCCkJMBiTAgBAxhBSEsF9UgAAyBhCSiK6x6RwugcAgPQjpCQi0pNCSAEAIN0IKYnoMSbFDIWy2xYAAE5xhJREhHtSJOlEW/baAQDAaYCQkgDD7ZZc3Y87YlwKAABpRUhJFONSAADICEJKojzc0A0AgEwgpCSqoPsyZO6VAgBAWhFSEhUJKfSkAACQToSURHWPSTE53QMAQFoRUhJ08vk9hBQAANKJkJKoyJOQGZMCAEA6EVIS5WFMCgAAmUBISVQBlyADAJAJhJRE9Xh+DwAASB9CSqK4ugcAgIwgpCTIYEwKAAAZQUhJFGNSAADICEJKohiTAgBARhBSEkVPCgAAGUFISVSPMSmmaWa3LQAAnMIIKYkK96SEQlJ7e3bbAgDAKYyQkqj8Askwul5zhQ8AAGlDSEmQYRiMSwEAIAMIKckIj0s5wRU+AACkCyElGfSkAACQdoSUZETulUJIAQAgXQgpySjoOt1jBjjdAwBAuiQUUg4ePKi77rpLN954o37xi19Yuk/Irl27dPvtt+vmm2/Wk08+GXOZjo4Off3rX9ff/va3RJqTNUYBPSkAAKSb5ZASDAZVU1Oj8vJyrVq1SvX19dq2bVvcdXw+n2pqajRjxgytWLFCO3bsUF1dXZ/lfve73+nQoUMJNz5rPIxJAQAg3SyHlJ07d8rv96u6ulper1cLFizQ1q1b466zY8cODR06VPPmzVNZWZnmz5/fZ50jR45oy5YtGj58eHKfIBvoSQEAIO1cVhc8cOCAJkyYoPz8fEnSuHHjVF9fP+A6U6ZM6bq3iKSKigqtX78+apkf//jH+tSnPqXXX399wDYEg0EFg8HItGEY8nRfDhzehx3C2+p3mz0eMmjnfge7AeuGmKhb4qhZcqhbcqhbcuyom+WQEggEono7DMOQw+FQa2uriouLY67j9/s1evToyLTH41FLS0tk+tlnn5Xf79fVV19tKaRs2rRJGzdujEyXl5erpqYmbb0wXq835vxjI0boPUkemRpWVpaWfQ9m/dUN8VG3xFGz5FC35FC35KRSN8shxeFwyO12R83Ly8tTe5zn1zidTrlcrpjL+3w+rV+/Xt/4xjfkdDottWHu3LmaM2dOZDqczpqbm9XR0WH1owzIMAx5vV41NjbGHBwcau/aV+Boi44cOWLbfge7geqG2Khb4qhZcqhbcqhbcuLVzeVyWepgsBxSiouL+wxuDQQCUSEk1jo+ny/m8j//+c81c+ZMnXPOOVabILfb3ScohaXjwDFNM+Z2zcglyDwJOZb+6ob4qFviqFlyqFtyqFtyUqmb5ZBSUVERNei1qalJwWCw31M9kjR+/Hg9//zzken9+/ertLRUkvTcc8/J4/Hov/7rvyRJbW1tuu+++zRv3jxdc801iX6OjDIKCmVKXN0DAEAaWQ4pkyZNkt/v1/bt23XppZdq8+bNmjp1qhwOh/x+v/Ly8vr0qkybNk0//elPVVdXp0mTJmnLli2qrKyUJK1ZsyZq2R/84Ae68sordcEFF6T+qdIt/Oweru4BACBtLIcUp9OpJUuWqLa2VuvWrVMoFNKyZcskSUuXLlV1dbWqqqqi1ikpKdHixYu1cuVKFRYWqqCgQLfccosk6eyzz45a1u12a8iQISoqKkrxI2UAz+4BACDtLIcUSaqqqlJtba327t2riRMnqqSkRJL04IMP9rvO7NmzVVlZqfr6ek2ePFmFhYUxlwsHnkGhxyXIAAAgPRIKKZJUWloaGVdildfrPbUu3Qr3pATbZXYEZbhiD+YFAADJ4wGDyei+ukcSvSkAAKQJISUJhtMp5XXdeZdxKQAApAchJVmMSwEAIK0IKcniCh8AANKKkJKsAu6VAgBAOhFSktV9usekJwUAgLQgpCSrgDEpAACkEyElSQa3xgcAIK0IKcli4CwAAGlFSEkWlyADAJBWhJRkRcak0JMCAEA6EFKS1T0mxQzQkwIAQDoQUpJFTwoAAGlFSEmS4WHgLAAA6URISRb3SQEAIK0IKckK3yeFnhQAANKCkJIsxqQAAJBWhJRk9bhPihkKZbctAACcgggpyQr3pEjSibbstQMAgFMUISVJhtstuVxdE4xLAQDAdoSUVBTwkEEAANKFkJIKHjIIAEDaEFJSwb1SAABIG0JKKjyc7gEAIF0IKano7kkxOd0DAIDtCCkpiDy/h54UAABsR0hJRWTgLGNSAACwGyElFYxJAQAgbQgpqeASZAAA0oaQkgoPlyADAJAuhJRUcHUPAABpQ0hJgcGYFAAA0oaQkgrGpAAAkDZZDyktLS363//9XwUG42W8jEkBACBtXIksfPDgQa1du1aNjY2aOXOmFi1aJMMw4q6za9cuPfzww/L5fJo7d67mzJkTeW/Lli367W9/q2HDhqm5uVl33HGHJk+enNwnyQZ6UgAASBvLPSnBYFA1NTUqLy/XqlWrVF9fr23btsVdx+fzqaamRjNmzNCKFSu0Y8cO1dXVSZIaGhr0u9/9Tg888IC++93v6uqrr9YTTzyR0ofJuB5jUkzTzG5bAAA4xVgOKTt37pTf71d1dbW8Xq8WLFigrVu3xl1nx44dGjp0qObNm6eysjLNnz8/sk4oFNIXvvAFDR06VJJ0zjnn6Pjx4yl8lCwI96SEQlJ7e3bbAgDAKcby6Z4DBw5owoQJys/PlySNGzdO9fX1A64zZcqUyCmhiooKrV+/XpI0evRojR49WpLU1tamP/zhD6qqqoq7vWAwqGAwGJk2DEOe7t6MgU47JSK8rQG3WeCRDEMyTRknAjIKCmxrw2BkuW6IQt0SR82SQ92SQ92SY0fdLIeUQCCg4cOHR+3c4XCotbVVxcXFMdfx+/2RICJJHo9HLS0tUcu89tprWr16tYYPH65rr702bhs2bdqkjRs3RqbLy8tVU1MT1S47eb3eAZepLyySebxVw88olrusLC3tGGys1A19UbfEUbPkULfkULfkpFI3yyHF4XDI7XZHzcvLy1N7nNMcTqdTLpcr7vKVlZX613/9Vz3yyCNat26dPvvZz/a7vd4Db8PprLm5WR0dHVY/yoAMw5DX61VjY+OAY03M/ALpeKuaDx6Q4XDHXfZUl0jdcBJ1Sxw1Sw51Sw51S068urlcLksdDJZDSnFxsQ4dOhQ1LxAIRIWQWOv4fL64yzudTp133nm68cYbVVNTEzekuN3uPkEpLB0HjmmaA283ctfZ4xIHrySLdUMf1C1x1Cw51C051C05qdTN8sDZiooK7dmzJzLd1NSkYDDY76keSRo/fnzUOvv371dpaakk6bnnntOWLVsi7zmdTjkcWb9tS+Ii90rhMmQAAOxkORVMmjRJfr9f27dvlyRt3rxZU6dOlcPhkN/vj3m6Zdq0adq9e7fq6urU2dmpLVu2qLKyUpI0cuRI/frXv9Yrr7yipqYmbdiwQR/5yEds+lgZVNA1cNccjDejAwAgh1k+3eN0OrVkyRLV1tZq3bp1CoVCWrZsmSRp6dKlqq6u7nN1TklJiRYvXqyVK1eqsLBQBQUFuuWWWyRJ5557rj73uc/p0Ucfld/v10UXXaTq6mr7PlmGGAWFMiV6UgAAsFlCd5ytqqpSbW2t9u7dq4kTJ6qkpESS9OCDD/a7zuzZs1VZWan6+npNnjxZhYWFkfcuueQSXXLJJUk2PUd4uOssAADpkFBIkaTS0tLIuBKrvF7vqXvpVgFjUgAASIdBOFI1x3SPSRFjUgAAsBUhJVU9nt8DAADsQ0hJVeQ+KYQUAADsREhJVeQ+KZzuAQDAToSUFBkFXN0DAEA6EFJSxZgUAADSgpCSKnpSAABIC0JKqhiTAgBAWhBSUhXuSQm2y+wIZrctAACcQggpqQrfzE2iNwUAABsRUlJkOJ1SXn7XBONSAACwDSHFDoxLAQDAdoQUO3CFDwAAtiOk2KGAe6UAAGA3QoodPDy/BwAAuxFS7FDAmBQAAOxGSLGBwa3xAQCwHSHFDvSkAABgO0KKHTxc3QMAgN0IKXaI9KQQUgAAsAshxQ7dY1LMAKd7AACwCyHFDvSkAABgO0KKDQzGpAAAYDtCih24ugcAANsRUuwQvk8KPSkAANiGkGIHxqQAAGA7QoodIg8YDMgMhbLbFgAAThGEFDuEB85K0om27LUDAIBTCCHFDi635HR1vWZcCgAAtiCk2MAwjJODZxmXAgCALQgpdingXikAANiJkGIX7pUCAICtCCl24XQPAAC2IqTYpbsnxeR0DwAAtnAlsvDBgwe1du1aNTY2aubMmVq0aFHXoNE4du3apYcfflg+n09z587VnDlzIu/9z//8jzZs2KBjx47p/PPP15e//GUNHTo0uU+SZYanUKZETwoAADax3JMSDAZVU1Oj8vJyrVq1SvX19dq2bVvcdXw+n2pqajRjxgytWLFCO3bsUF1dnSRp9+7deuKJJ3TrrbdqzZo1CgaD+sUvfpHSh8mqyMBZxqQAAGAHyyFl586d8vv9qq6ultfr1YIFC7R169a46+zYsUNDhw7VvHnzVFZWpvnz50fWaWho0Oc+9zl98IMf1LBhw/SJT3xCe/fuTe3TZBNjUgAAsJXl0z0HDhzQhAkTlJ+fL0kaN26c6uvrB1xnypQpkVNCFRUVWr9+vSRp5syZUcs2NDTI6/XG3V4wGFQwGIxMG4YhT3c4GOi0UyLC20pkm4anqPt0T8DWtgwmydQN1C0Z1Cw51C051C05dtTNckgJBAIaPnx41M4dDodaW1tVXFwccx2/36/Ro0dHpj0ej1paWvosd+zYMf3P//yP/vmf/zluGzZt2qSNGzdGpsvLy1VTUxPVLjsNFJp6OjbCq/ckFSiks8rK0tKewSKRuuEk6pY4apYc6pYc6pacVOpmOaQ4HA653e6oeXl5eWpvb+93HafTKZfLNeDyP/nJTzRhwgRdeOGFcdvQe+BtOJ01Nzero6PD0uewwjAMeb1eNTY2yjRNS+uE2rt6eNpaWnTkyBHb2jKYJFM3ULdkULPkULfkULfkxKuby+Wy1MFgOaQUFxfr0KFDUfMCgUBUCIm1js/ni7v81q1b9fe//13f+c53BmyD2+3uE5TC0nHgmKZpfbvdT0I22/yn/UGcUN0QQd0SR82SQ92SQ92Sk0rdLA+craio0J49eyLTTU1NCgaD/Z7qkaTx48dHrbN//36VlpZGpt988009+uij+upXv6ohQ4Yk2PQcw23xAQCwleWQMmnSJPn9fm3fvl2StHnzZk2dOlUOh0N+vz/m6ZZp06Zp9+7dqqurU2dnp7Zs2aLKykpJ0nvvvaeamhp96lOf0rnnnqu2tja1tbXZ9LGywMNt8QEAsJPl0z1Op1NLlixRbW2t1q1bp1AopGXLlkmSli5dqurqalVVVUWtU1JSosWLF2vlypUqLCxUQUGBbrnlFknS888/r/fff1+PP/64Hn/88cg6GzZssOFjZQHP7gEAwFYJ3XG2qqpKtbW12rt3ryZOnKiSkhJJ0oMPPtjvOrNnz1ZlZaXq6+s1efJkFRZ2/TK/6qqrdNVVV6XQ9BzT4z4ppmlyqRoAAClKKKRIUmlpadS4Eiu8Xu+pf+lWuCels1MKtkt5+dltDwAAgxwPGLRLfoEU7j3hrrMAAKSMkGITwzB4fg8AADYipNiJ5/cAAGAbQoqduFcKAAC2IaTYKXKvFEIKAACpIqTYKXxrfMakAACQMkKKnQoYkwIAgF0IKTYyGJMCAIBtCCl2YkwKAAC2IaTYifukAABgG0KKnbhPCgAAtiGk2Km7J8VkTAoAACkjpNgpMiaF0z0AAKSKkGIjru4BAMA+hBQ7MSYFAADbEFLsRE8KAAC2IaTYiTEpAADYhpBip3BPSrBdZkcwu20BAGCQI6TYKfzsHoneFAAAUkRIsZHhdEp5+V0TjEsBACAlhBS7MS4FAABbEFLsxhU+AADYgpBitwLulQIAgB0IKXbrPt1jcroHAICUEFLsFj7dQ08KAAApIaTYzAjfGj9ATwoAAKkgpNiNnhQAAGxBSLGbh6t7AACwAyHFbvSkAABgC0KK3brHpJiMSQEAICWEFLt5irq+H30nu+0AAGCQI6TYzJgwRTIMad//yWxpznZzAAAYtAgpNjOGDpM+MFmSZP7puSy3BgCAwYuQkgbG9I9LksxXdmS5JQAADF4JhZSDBw/qrrvu0o033qhf/OIXMk1zwHV27dql22+/XTfffLOefPLJPu83NjbqxhtvTKQZOc+48GLJ4ZAOvCmzqSHbzQEAYFCyHFKCwaBqampUXl6uVatWqb6+Xtu2bYu7js/nU01NjWbMmKEVK1Zox44dqquri7zf1NSkVatW6fjx40l/gFxklAyRzquUJJmvcsoHAIBkWA4pO3fulN/vV3V1tbxerxYsWKCtW7fGXWfHjh0aOnSo5s2bp7KyMs2fPz9qnVWrVunyyy9PvvU5zKjqPuXzKqd8AABIhsvqggcOHNCECROUn58vSRo3bpzq6+sHXGfKlCkyDEOSVFFRofXr10fev/POO2UYhtatW2epDcFgUMFgMDJtGIY83fclCe/DDuFtpbJNx4UfVecvfigdPiA1HJQxapxdzctZdtTtdETdEkfNkkPdkkPdkmNH3SyHlEAgoOHDh0ft3OFwqLW1VcXFxTHX8fv9Gj16dGTa4/GopaUlMj1ixAg1NTVZbuymTZu0cePGyHR5eblqamqi2mUnr9eb0vrN0y5W28t/VNHfX9eZ0z5iU6tyX6p1O11Rt8RRs+RQt+RQt+SkUjfLIcXhcMjtdkfNy8vLU3t7e7/rOJ1OuVwuy8sPZO7cuZozZ05kOpzOmpub1dHRkfR2ezMMQ16vV42NjZYGB/cn9MEq6eU/yvfs73X88n865VO4XXU73VC3xFGz5FC35FC35MSrm8vlstTBYDmkFBcX69ChQ1HzAoFAVAiJtY7P57O8/EDcbnefoBSWjgPHNM3UtvvB6VJenvR2g8wDe6Vx4+1rXA5LuW6nKeqWOGqWHOqWHOqWnFTqZnngbEVFhfbs2ROZbmpqUjAY7PdUjySNHz8+ap39+/ertLQ0qYYORkaBR8bU6ZIk89U/Zrk1AAAMLpZDyqRJk+T3+7V9+3ZJ0ubNmzV16lQ5HA75/f6Yp1umTZum3bt3q66uTp2dndqyZYsqKyvta/0gcPIqn+dkhkJZbg0AAIOH5ZDidDq1ZMkSPfzww/r85z+vl19+Wddff70kaenSpXrttdf6rFNSUqLFixdr5cqVWrJkiQ4dOqRrr73WvtYPBlM+LBV4pJZm6a3/zXZrAAAYNBIaIFJVVaXa2lrt3btXEydOVElJiSTpwQcf7Hed2bNnq7KyUvX19Zo8ebIKCwuj3j/77LO1YcOGJJo+OBh5+TIu+IjMl56V+eoOGRWTst0kAAAGhYSf3VNaWqrp06dHAooVXq9X06ZN6xNQTheRUz5/ek5mqDPLrQEAYHDgAYOZMKlSKjpD8r0n/W/dgIsDAABCSkYYLreMCz8qidvkAwBgFSElQ4zp3ad8XntRpo03ngMA4FRFSMmUiVOkkiHS8WPS39/IdmsAAMh5hJQMMRxOGR+eIYkbuwEAYAUhJYMiV/nsfElmMPlnGAEAcDogpGTSuedJpWdJbQHpr3/OdmsAAMhphJQMMhwOGdPCt8nnKh8AAOIhpGRY5JTPX16R2RbIcmsAAMhdhJRMGzteOrtMam+X+cYr2W4NAAA5i5CSYYZhnLxnCqd8AADoFyElC4zpl3S9qHtN5vHW7DYGAIAcRUjJAmPUWGnUOKmzQ+brL2W7OQAA5CRCSpYY0z4miVM+AAD0h5CSJeFxKfr7GzKPvZ/dxgAAkIMIKVlijBgpjauQQiGZf34h280BACDnEFKyiKt8AADoHyEli8LjUrTnbzKPvpvdxgAAkGMIKVlkDBsuVUySTFPmn5/LdnMAAMgphJQsi5zyeYVTPgAA9ERIyTLjwzMkwyHt+z+ZzY3Zbg4AADmDkJJlxplDpfOmSpLMP3HKBwCAMEJKDojc2I1TPgAARBBScoBx4Uclp1Oq3yfzSH22mwMAQE4gpOQAo7hEmvwhSdwzBQCAMEJKjuh5YzfTNLPcGgAAso+QkiOMCy6SXG6psV6q35/t5gAAkHWElBxheAqlD06TJJmv/jHLrQEAIPtc2W7AYGKapk50mvIHQ/K3d3Z9D4bkD3a9DnRPt3WENKTApVEleRp5Rp5GFLvldBgDbt8x/eMKvfaizFd2yLzmBhkOMiQA4PRFSInhBy82qM1sUssxf3f46NTx7hASSmK4iNOQvGfkRULLqJI8jTojTyNL8jSkwCnD6A4wU6dL+QXSu00KfXWhNOZcGePGS+MqZIwdL40YSXABAJw2CCkx7Gw4rpZAR7/vOwzJ43ao0OVQYZ5ThW5Hjy+n8lyGWvwdajjWrsO+drV3mjrs63rdW6HbEQkuI0vyNPLym1X60n+rIxRS+zsdCrbsVfsbBxR0bFcwr1DB0rMVLD1b7WcOU/CMUgULihQMSe2dIQVDpoKdpkxJhiSHYchhSIbR/VonX3d9lwx1LdO1XPi1IaPnspHX3ct0T4dfOxzd23EYOrOhQ8d8Pim8TI/tSCenu74b/U8bkkNdbzi6V3TIiNpm3/W72mF0L2+ox3a7c2B4OenkZ1PvNsTbp07Wruc2ercn5vvdtQ4fQ5FwCgCIyTBPgUtJmpubFQwGbdve1rfeV8mZZyroPyaP62QA8XSHkAKXYfkXTMg09a6/Q4d97ZHQ0tD9uul4MKmeGZw6egYcmWZ3gIoOT72XlYxe0+oTzNQdIHvPM2IsG9mnodihrfe87pWtBM5wSB4olDq629A7xPVePhIeDUOFhR61BQKRzx8Jwhbb1l847rOv3mG5R1gN1yb2vnsH3JPBtPfPrvf86J9Rj3r3mh/7+IjeXu+f57Bhw9Ty7rtR6/V8YcScF72veAZaLLrNRtQ8I8Zy0fN6fC6jb1sjf6TEWD/efmO1r/d+vSNG6O2331b4v+zeq/bZlBF78mR7jTjv9d/2WO3v05Y+840+7/Xb7hjL9TxGE2EYhsrKynTkyJE+V6263W4NHz584G0QUvqKV1g7tXeG1NgajASXcJA5GuhQntOQ2+no/m7IbUjuYJvyAq1yH/fJfeyo3MdalBdskzvUKXcoqLxQh1yhDjlkKiRDIcMh0zBkdr8OOZ0yDYdCDpdMh0Mhh1Omw6mQw6GQ4eyaZzhkdn+FDKNrfcOhkPq+DhmOyLa7pg05nC51doYUMrr++wjv3zTU9T3y2nFyXveyIfVepvt19/xQ9z+SkE5+73ov/Lprfte66tq/Yr8Odf8MwtsOvw6Z6rHPHl/myXVCkkyz5/uJ/+MFgMHi59dWaKgn8RMvdoQUTvdkUZ7TobFn5mvsmflJrW+GOqXGwzIP7JUOvNn1/dA+6UTA5pZiIJHAFAllJ8OVaTii3+sOXCfnOSJ/nZlGODCdDE7qsS31WC5qujvYST33ezLoqefr8PrdXQfhUHrytSFFwqoh09EVRuVwRIJqeN2Qw9ndPkdkm6bDefJ1OMh2LxcJzn2+99qGer3X+33DkCsvX+3BjuiahwNv1M+hn/e6axtSrHUUmRf++Z5ctkdQDofxHvNMw+gKsb1CdeTnEn7d42cd+flH/ez7m68e82PN635tGlHzw+85XS51dHRKMqOPs/ALoyuER7732oa6axP+1v/fcX3fCIf7Pvvs9RnCG41atvdyPZbpuS2zxyZ6bqW/z9J3vhnnc0ev0+sjIA0SCikHDx7U2rVr1djYqJkzZ2rRokUDdgHt2rVLDz/8sHw+n+bOnas5c+ZYeg8DMxxOaeRYGSPHSh+9LDLfDIWkUKfU2eMr1Cl1dnS/DnW97m8ZU5IZ6vqXGDIls7P7uymZoa5EHOp+P/I6JMM0dWZJid5/772uNoS3YYai1lfv9XvuSz2Wl9lrfo/1FG5bd/9GqDPc5XFy2RhfZs91Yy4XOvn5pa79SX3b1OvLYYb63WfUZ+xnvsNwKNTZEbsdMad7fAHZZjh6nKvqPgkTOcfk0MlzM8bJ1z2XCb9W721YWU599xlvud5t7D2/9zYdXd/z8/PVHgx2/R8Sc9/qu93u9yOBNDI//EdFeB1H9x8S3adyjJMhWj2n+6wX/UdIz/M8J/8gcUSfR+qnDb3/6OlZlzPMMcpWn4blvQaDQdXU1KiyslK33Xabfvazn2nbtm267LLL+l3H5/OppqZGV199tWbMmKHVq1frnHPO0ZQpU+K+h9QYDkfXaFaXOz3b72++Yai4rEzH0nya7FST6ulFM9R1oix2sOsv8PUMjv0FqZ5BtZ+wFDNs9poXih1WzVB/++sVDM1e+w919TGcUVSkYz5fr+301+Ze82LWLMbyPd4z+w2bvWscI0gnFFoV4/04ITUqsMYJ4L2DvZ3C7TuFnUhhXaPX98HGceU1koqysm/LIWXnzp3y+/2qrq5Wfn6+FixYoJ/+9KdxQ8qOHTs0dOhQzZs3T4ZhaP78+dq6daumTJkS9z0A1kUuSx9kV6en8h+2YRgqKSvTcQJxQnoG4lDPMBUJQFKfgBN5v+d0rPdDFpfr8TqyTI/XVpcL/9wH+gx9lundNmv7HjpkiI4ePdr9R4HibDfO/nXyW995PZaNHNI9ttH7XFbvdc3+ttXj30fv9axuJ79A2WI5pBw4cEATJkxQfn7X+Ilx48apvj7+E3sPHDigKVOmRE4JVVRUaP369QO+159gMBg1QNYwDHk8nshru0RGkNu4zdMBdUsOdUscNUtOz7o5uOeSZYZhqNDrla+xkVCcADv+nVoOKYFAIGokbvggb21tVXFxccx1/H6/Ro8eHZn2eDxqaWkZ8L3+bNq0SRs3boxMl5eXq6amxtII4WR4vd60bPdUR92SQ90SR82SQ92SQ92Sk0rdLIcUh8Mhtzt6jENeXp7a2/veoCzM6XTK5XLFXD7ee/3pPbg2nM6am5vV0dH/zdcSZRiGvF6vGknNCaFuyaFuiaNmyaFuyaFuyYlXN5fLZe8lyMXFxTp06FDUvEAgEBU0Yq3j6777aO/l473XH7fb3ScohaXjwDFNkwMyCdQtOdQtcdQsOdQtOdQtOanUzfJJyYqKCu3Zsycy3dTUpGAw2O+pHkkaP3581Dr79+9XaWnpgO8BAABYDimTJk2S3+/X9u3bJUmbN2/W1KlT5XA45Pf7Y55umTZtmnbv3q26ujp1dnZqy5YtqqysHPA9AAAAy6d7nE6nlixZotraWq1bt06hUEjLli2TJC1dulTV1dWqqqqKWqekpESLFy/WypUrVVhYqIKCAt1yyy0DvgcAAJDws3taWlq0d+9eTZw4USUlJZbWaWxsVH19vSZPnqzCwkLL71k1WJ/dc6qhbsmhbomjZsmhbsmhbsnJyrN7SktLEx474vV6+70EKd57AADg9MXdfAAAQE4ipAAAgJxESAEAADmJkAIAAHISIQUAAOSkhK/uyUUD3U4/17Z7qqNuyaFuiaNmyaFuyaFuyYlVN6u1TPg+KQAAAJnA6Z4YAoGA7rjjDgUCgWw3ZVChbsmhbomjZsmhbsmhbsmxo26ElBhM09S+ffu4s2CCqFtyqFviqFlyqFtyqFty7KgbIQUAAOQkQgoAAMhJhJQY3G635s+fL7fbne2mDCrULTnULXHULDnULTnULTl21I2rewAAQE6iJwUAAOQkQgoAAMhJhBQAAJCTuMcvbPHII4/oqaeeikyPGDFC//7v/57FFuFUc+zYMd1555265557dPbZZ0viuIP9Xn31VT366KN65513dO655+pLX/qSRo8ezbFmwbFjx9TQ0KCysjKVlJTYsk1CSi8HDx7U2rVr1djYqJkzZ2rRokUyDCPbzcp5b731lu68805NnDhRkuRw0EnXn1i/bDnu4vP5fKqpqVFzc3PUfI67+Pr7hcvxFltjY6N++MMf6vOf/7wmT56sRx55RA899JC+/e1vc6wN4Pnnn9dPfvITDR8+XA0NDfriF7+oGTNmpHysUeUegsGgampqVF5erlWrVqm+vl7btm3LdrNyXmdnpw4dOqTJkyerqKhIRUVF8ng82W5WTvL5fLrvvvuiftly3A3sBz/4gWbMmBE1j+MuvvAv3IULF+pHP/qRzjrrLD300EMcb3EcPnxYCxYs0MUXX6whQ4Zo1qxZ2rt3L8faAI4fP65HHnlEy5cv13e+8x0tWbJEv/zlL2051ggpPezcuVN+v1/V1dXyer1asGCBtm7dmu1m5bwDBw7INE0tXbpU119/vVauXKl33nkn283KSbF+2XLcDWzJkiW68soro+Zx3MXX3y9cjrf+ffjDH9asWbMi0w0NDfJ6vRxrAwgEAvrsZz+rsWPHSpLGjRun48eP23KsEVJ6OHDggCZMmKD8/HxJXYWur6/Pcqty3+HDhzVmzBjddttt+v73vy+n06kf//jH2W5WTurvly3HXXwjRozoM4/jLr54v3A53gbW0dGhLVu2aNasWRxrAzjrrLP08Y9/XNLJulVVVdlyrDEmpYdAIKDhw4dHpg3DkMPhUGtrq4qLi7PYstz28Y9/PHKAStLNN9+sW2+9VX6/X4WFhVlsWe6J9cuW4y45HHfWhX9xXHXVVWpqauJ4s+Dxxx9XQUGB/uEf/kEul4tjzYL9+/dr+fLlcrlcWr16tX7zm9+kfKzRk9KDw+Hoc/vevLw8tbe3Z6lFg1NRUZFM09R7772X7aYMChx39uC461/PX7gcbwP7y1/+oqefflq33XabXK6+f8tzrMU2btw43X333RozZox++MMf2nKsEVJ6KC4uls/ni5oXCARiHqQ46dFHH9ULL7wQmX7zzTdlGIaGDRuWxVYNHhx3yeG4s6b3L1yOt/jefvtt1dbW6nOf+5xGjx4tiWPNKsMwVF5eri996Uv605/+ZMuxxlHZQ0VFRdSgnqamJgWDQbpAB3DOOefo8ccf15AhQxQKhfSzn/1Mn/jEJyLnIREfx11yOO4GFusXLsdb/9rb23Xfffdp+vTpmj59utra2iR19RBwrPWvrq5OO3fu1A033CBJcjqdkqRRo0alfKwRUnqYNGmS/H6/tm/frksvvVSbN2/W1KlTuR5+AJdeeqkOHz6s+++/XwUFBaqqqtKCBQuy3axBg+MuORx38fX3C/e8887jeOvH66+/rsOHD+vw4cN65plnIvPXrFmjhoYGjrV+jBw5Uvfff7/Kysp0wQUX6PHHH9cHP/hBXXjhhXrooYdSOtZ4CnIvr7zyimpra+XxeBQKhbRs2TKNGTMm283CKebTn/601qxZE7mZG8cd7PbKK6/ou9/9bp/5a9as0f79+zneYKvXX39djz76qFpaWlRZWanPfe5zKikpSfn/NkJKDC0tLdq7d68mTpxo2619gYFw3CGTON6QKakca4QUAACQkzgJCQAAchIhBQAA5CRCCgAAyEmEFAAAkJMIKQAAICcRUgAAQE4ipAAAgJxESAEAADmJkAIAAHLS/w88BagInQ9JhgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 2.08218\n",
      "均方根误差: 1.44298\n",
      "平均绝对误差: 1.06879\n",
      "R2: 0.32956\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
